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DeepSeek-R1 vs Magistral Small 2506
Comprehensive side-by-side LLM comparison
DeepSeek-R1 is available on 5 providers. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-R1 was developed as a reasoning-focused language model, designed to combine chain-of-thought reasoning with reinforcement learning techniques. Built to excel at complex problem-solving through trial-and-error learning and deliberate analytical processes, it demonstrates the power of efficient training methods in open-source model development.
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Mistral AI
Magistral Small was created as an efficient reasoning-focused variant, designed to bring analytical capabilities to applications with tighter resource constraints. Built to balance problem-solving depth with practical deployment needs, it extends reasoning-enhanced AI to broader use cases.
4 months newer
DeepSeek-R1
DeepSeek
2025-01-20
Magistral Small 2506
Mistral AI
2025-06-10
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Performance Metrics
Context window and performance specifications
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Knowledge Cutoff
Training data recency comparison
Magistral Small 2506
2025-06-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-R1
5 providers
DeepInfra
Throughput: 0.9 tok/s
Latency: 0.3ms
DeepSeek
Throughput: 9 tok/s
Latency: 0.3ms
Fireworks
Throughput: 2.1 tok/s
Latency: 0.3ms
Together
Throughput: 4 tok/s
Latency: 0.6ms
ZeroEval
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DeepSeek-R1
Avg Score:0.0%
Providers:5
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Magistral Small 2506
Avg Score:0.0%
Providers:0